The present invention is in the field of medical devices to treat neurological disorders of the brain. More specifically, the invention is directed to a method and a partially or fully implanted apparatus for predicting and detecting epileptic seizure onsets within a unified multiresolution probabilistic framework, thereby enabling a portion of the device to automatically deliver a progression of multiple therapies, ranging from benign to aggressive as the probabilities of seizure warrant, in order to deter the course of a seizure with only the minimally required intervention and associated side effects.
Second to stroke, epilepsy is the most common neurological disorder of the brain. It is characterized by recurrent seizures that significantly impair the quality of life of an estimated 1 to 2% of the world's population. Drugs are the most common form of treatment, but their efficacy is limited. Up to 30% of patients achieve no control of their seizures with drugs, and another 30% experience severe side effects that make it impossible to lead normal lives.
A personal device capable of warning and/or intervening therapeutically in response to imminent seizures would allow those with epilepsy to, at a minimum remove themselves from danger (e.g., stop driving a car), and in the best case become seizure-free, not even noticing times when they were about to have a seizure. Such a device would operate in a continuous-time closed control loop where therapy is responsive to measurements (this includes a patient's own actions in the loop).
Several prior art closed-loop responsive systems with applicability to improving the quality of life of epileptic individuals are known to have been proposed in the field to date. All prior art systems share the following disadvantages: (1) they only detect visually obvious changes in raw signals thus control of seizures is attempted only after individuals actually begin having each seizure; (2) they implement a deterministic approach which is inadequate in face of the uncertainty and complexity of the problem; (3) they offer no means of gauging confidence in the outputs; (4) they implicitly assume a single (infinite) time resolution which may be adequate for seizure detection but not prediction; (5) they suggest a control scheme which is closed-loop only at the triggering instant dictated by detection (treatment beyond that point is open-loop, and is called triggered open-loop control in the present invention); (6) they do not deliver therapy that is graded from benign to aggressive as the situation warrants; (7) they do not consider side effects; (8) they imply detection schemes that are not guided by optimality criteria; (9) they rely on a single input feature or multiple features of the same nature (e.g., power in frequency bands) or only few uncorrelated features; (10) they use the same features for the whole patient population and do not take advantage of patient-specific features; (11) they do not transfer adequate samples of data for offline analysis; (12) they possess little or no computational intelligence with no learning capabilities to automatically improve and maintain performance over time; (13) they directly threshold separate single features instead of an implicit likelihood ratio function of joint features thereby yielding suboptimal decision rules; and (14) they do not account for the fact that training and/or testing seizure detectors/predictors with wrong prior probabilities of seizures/preseizures (as reflected in raw data archives or clinical trials) induces a variety of distortions that must be corrected.
The present invention is directed to overcome the disadvantages and limitations of all prior art.